NVIDIA Corporation (NASDAQ:NVDA) Goldman Sachs Communacopia + Technology Conference September 11, 2024 10:20 AM ET

NVIDIA 公司 (纳斯达克:NVDA) 高盛 Communacopia + 技术会议 2024 年 9 月 11 日 上午 10:20 ET

Company Participants 公司参与者

Jensen Huang - President and CEO

黄仁勋 - 总裁兼首席执行官

Conference Call Participants

会议电话参与者

Toshiya Hari - Goldman Sachs

Toshiya Hari - 高盛

Toshiya Hari 敏也针

Good morning, good morning.

早上好，早上好。

Jensen Huang 黄仁勋

Thank you. Good morning. 谢谢。早上好。

Toshiya Hari 敏也针

How is everybody feeling?

大家感觉怎么样？

Jensen Huang 黄仁勋

Great to see everybody. 很高兴见到大家。

Toshiya Hari 敏也针

I flew in late last night. I didn't really expect to be on stage at 7:20 in the morning, but seems everybody else did. So, here we are. Jensen, thank you for being here. I'm delighted to be here. Thank you all for being here. I hope everybody has been enjoying the conference. It's a fantastic event, lots of great companies, a couple of thousand people here. And so really terrific. And obviously, a real highlight and a real privilege to have Jensen, President and CEO of NVIDIA here. Since you found NVIDIA in 1993, you've pioneered accelerating computing. The company's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefining computers and igniting the era of modern AI. Jensen holds a BSEE degree from Oregon State University and an MSEE degree from Stanford. And so I want to start by welcoming you, Jensen. Everybody, please welcome Jensen to the stage.

我昨晚很晚才到。我没想到早上 7:20 就要上台，但似乎其他人都这样认为。所以，我们在这里。詹森，谢谢你能来。我很高兴能在这里。感谢大家的到来。希望大家都在享受这次会议。这是一个很棒的活动，有很多优秀的公司，现场有几千人。所以真的很棒。显然，能够邀请到英伟达的总裁兼首席执行官詹森是一个真正的亮点和特权。自 1993 年创办英伟达以来，你一直在推动计算加速。公司在 1999 年发明的 GPU 引发了 PC 游戏市场的增长，重新定义了计算机，并点燃了现代人工智能的时代。詹森拥有俄勒冈州立大学的电气工程学士学位和斯坦福大学的电气工程硕士学位。所以我想先欢迎你，詹森。大家，请欢迎詹森上台。

Jensen Huang 黄仁勋

Thank you. 谢谢。

Question-and-Answer Session

问答环节

Q - Toshiya Hari Q - 俊也针

So we're going to try to do this really casually, and I'm going to try to get you talking about some things that I know you're passionate about. But I just want to start, 31 years ago, founded the company, you've transformed yourself from a gaming-centric GPU company to one that offers a broad range of hardware, software to the data center industry. And I'd just like you to start by talking a little bit about the journey. When you started, what were you thinking, how has it evolved because it's been a pretty extraordinary journey. And then maybe you can break from that, just talk a little bit as you position forward on your key priorities and how you're looking at the world going forward?

所以我们将尝试以非常随意的方式进行，我会尽量让你谈论一些我知道你热衷的事情。但我想先从 31 年前开始，创立公司时，你将自己从一个以游戏为中心的 GPU 公司转变为一个为数据中心行业提供广泛硬件和软件的公司。我希望你能先谈谈这段旅程。当你开始时，你在想什么，这个过程是如何演变的，因为这是一段相当非凡的旅程。然后，也许你可以从中抽离出来，谈谈你在前进过程中关注的关键优先事项，以及你如何看待未来的世界？

Jensen Huang

詹森 黄

Yeah, David, it's great to be here. The thing that we got right, I would say, is that we -- our vision that there would be another form of computing that could augment general purpose computing to solve problems that a general purpose instrument won't ever be good at. And that processor would start out doing something that is -- that was insanely hard for CPUs to do and it was computer graphics, but that we would expand that over time to do other things. The first thing that we chose, of course, was image processing, which is complementary to computer graphics. We extended it to physics simulation because in the domain -- the application domain that we selected video games, you want it to be beautiful, but you also want it to be dynamic to create virtual worlds. We took step by step by step and we took it into scientific computing beyond that. One of the first applications was molecular dynamic simulation. Another was seismic processing, which is basically inverse physics. Seismic processing is very similar to CT reconstruction, another form of inverse physics. And so we just took it step by step by step, reasoned about complementary types of algorithms, adjacent industries, kind of solved our way here, if you will. But the common vision at the time was that accelerated computing would be able to solve problems that are interesting. And that if we were able to keep the architecture consistent, meaning I have an architecture where software that you develop today could run on a large installed base that you've left behind and the software that you created in the past would be accelerated even further by new technology. This way of thinking about architecture compatibility, creating large installed base, taking the software investment of the ecosystem along with us, that psychology started in 1993 and we carried it to this day, which is the reason why NVIDIA's CUDA has such a massive install base and then -- because we always protected it. Protecting the investment of software developers has been the number one priority of our company since the very beginning. And going forward, some of the things that we solved along the way, of course, learning how to be a founder, learning how to be a CEO, learning how to conduct a business, learning how to build a company...

Toshiya Hari 敏也针

Not easy stuff. 不容易的事情。

Jensen Huang 黄仁勋

These are all new skills and we’re just kind of like learning how to invent the modern computer gaming industry. NVIDIA -- people don't know this, but NVIDIA is the largest install base of video game architecture in the world. GeForce is some 300 million gamers in the world, still growing incredibly well, super vibrant. And so I think the -- every single time we had to go and enter into a new market, we had to learn new algorithms, new market dynamics, create new ecosystems. And the reason why we have to do that is because unlike a general purpose computer, if you built that processor, then everything eventually just kind of works. But we're an accelerated computer, which means the question you have to ask yourself is, what do you accelerate? There's no such thing as a universal accelerator because, yeah...

这些都是全新的技能，我们就像是在学习如何创造现代计算机游戏产业。NVIDIA——人们不知道的是，NVIDIA 是全球最大的电子游戏架构安装基础。GeForce 在全球有大约 3 亿玩家，仍在以惊人的速度增长，非常活跃。因此，我认为每次我们进入一个新市场时，我们都必须学习新的算法、新的市场动态，创造新的生态系统。我们之所以必须这样做，是因为与通用计算机不同，如果你构建了那个处理器，那么一切最终都会正常工作。但我们是加速计算机，这意味着你必须问自己，你要加速什么。没有所谓的通用加速器，因为，是的……

Toshiya Hari 敏也针

Dig down on this a little bit deeper, just talk about the differences between general purpose and accelerating computing.

深入探讨一下这个问题，谈谈通用计算和加速计算之间的区别。

Jensen Huang 黄仁勋

If you look at software, out of your body of software that you wrote, there's a lot of file IO, there's a -- setting up the data structure, there's a part of the software inside, which has some of the magic kernels, the magic algorithms. And these algorithms are different depending on whether it's computer graphics or image processing or whatever it happens to be. It could be fluids, it could be particles, it could be inverse physics as I mentioned, it could be image domain type stuff. And so all these different algorithms are different. And if you created a processor that is somehow really, really good at those algorithms and you complement the CPU where the CPU does whatever it's good at, then theoretically, you could take an application and speed it up tremendously. And the reason for that is because usually some 5%, 10% of the code represents 99.999% of the runtime. And so if you take that 5% of the code and you offloaded it on our accelerator, then technically, you should be able to speed up the application 100 times. And it's not abnormal that we do that. It's not unusual. And so we'll speed up image processing by 500 times. And now we do data processing. Data processing is one of my favorite applications because almost everything related to machine learning, which is a data-driven way of doing software, data processing has evolved. It could be SQL data processing, it could be Spark type of data processing, it could be a vector database type of processing, all kinds of different ways of processing either unstructured data or structured data, which is data frames and we accelerate the living daylights out of that. But in order to do that, you have to create that library, that fancy library on top. And in the case of computer graphics, we were fortunate to have Silicon Graphics' OpenGL and Microsoft DirectX. But outside of those, no libraries really existed. And so for example, one of our most famous libraries is a library kind of like SQL is a library. SQL is a library for in-storage computing. We created a library called cuDNN. cuDNN is the world's first neural network computing library. And so we have cuDNN, we have cuOpt for combinatory optimization, we have cuQuantum for quantum simulation and emulation, all kinds of different libraries, cuDF for data frame processing, for example, SQL. And so all these different libraries have to be invented that takes the algorithms that run in the application and refactor those algorithms in a way that our accelerators can run. And if you use those libraries, then you get 100x speed up.

Toshiya Hari 敏也针

You get much more speed.

你获得了更快的速度。

Jensen Huang 黄仁勋

Incredible. And so the concept is simple and it made a lot of sense, but the problem is, how do you go and invent all these algorithms and cause the video game industry to use it, write these algorithms, cause the entire seismic processing and energy industry to use it, write a new algorithm and cause the entire AI industry to use it. You see what I'm saying?

不可思议。这个概念很简单，也非常合理，但问题是，你如何去发明所有这些算法，并让视频游戏行业使用它，编写这些算法；让整个地震处理和能源行业使用它，编写新的算法；让整个AI行业使用它。你明白我的意思吗？

Toshiya Hari 敏也针

Yeah. 是的。

Jensen Huang 黄仁勋

And so these libraries -- every single one of these libraries, first, we had to do the computer science. Second, we have to go through the ecosystem development. And we have to go convince everybody to use it and then what kind of computers does it want to run on, all the different computers are different. And so we just did it one domain after another domain after another domain. We have a rich library for self-driving cars. We have a fantastic library for robotics, incredible library for virtual screening, whether it's physics based virtual screening or neural network based virtual screen, incredible library for climate tech. And so one domain after another domain. And so we have to go meet friends and create the market. And so what NVIDIA is really good at, as it turns out, is creating new markets. And we just -- we've done it for now so long that it seems like NVIDIA’s accelerated computing is everywhere, but we really had to do it one at a time, one industry at a time.

Toshiya Hari 敏也针

So, I know that many investors in the audience are super focused on the data center market. And it would be interesting to kind of get your perspective -- the company's perspective, on the medium and long-term opportunity set. Obviously, your industry is enabling, your term, the next industrial revolution. What are the challenges the industry faces? Talk a little bit about how you view the data center market as we sit here today.

所以，我知道在座的许多投资者非常关注数据中心市场。了解一下你们公司的观点，关于中长期的机会集将会很有趣。显然，你们的行业正在推动你们所说的下一个工业革命。这个行业面临哪些挑战？谈谈你们今天对数据中心市场的看法。

Jensen Huang 黄仁勋

There are two things that are happening at the same time, and it gets conflated, and it's helpful to tease apart. So the first thing, let's start with a condition where there's no AI at all. Well, in a world where there's no AI at all, general purpose computing has run out of steam still. And so we know that Dennard scaling, for all the people in the room that enjoy semiconductor physics, Dennard scaling and Mead-Conway's shrinking of transistors, scaling of transistors, and Dennard scaling of a ISO power increased performance, or ISO cost increasing performance, that -- those days are over. And so we're not going to see CPUs, general purpose computers that are going to be twice as fast every year ever again. We'll be lucky if we see it twice as fast every 10 years. Now, Moore's Law -- remember back in the old days, Moore's law was 10 times every five years, 100 times every 10 years. And so all we have to do is just wait for the CPUs to get faster. And as the world's data centers continue to process more information, CPUs got twice as fast every single year. And so we didn't see computation inflation, but now that's ended. We're seeing computation inflation. And so the thing that we have to do is we have to accelerate everything we can. If you're doing SQL processing, accelerate that. If you're doing any kind of data processing at all, accelerate that. If you're doing -- if you're creating an Internet company and you have a recommender system, absolutely accelerate it and they're now fully accelerated. This, a few years ago was all running on CPUs, but now the world's largest data processing engine, which is a recommender system, is all accelerated now. And so if you have recommender systems, if you have search systems, any large scale processing of any large amounts of data, you have to just accelerate that. And so the first thing that's going to happen is the world's trillion dollars of general purpose data centers are going to get modernized into accelerated computing. That's going to happen no matter what. That's going to happen no matter what. And the reason for that is, as I described, Moore's Law is over. And so the first dynamic you're going to see is the densification of computers. These giant data centers are super inefficient because it's filled with air, and air is a lousy conductor of electricity. And so what we want to do is take that few, call it, 50, 100, 200-megawatt data center, which is sprawling, and you densify it into a really, really small data center. And so if you look at one of our server racks, NVIDIA server racks look expensive, and it could be a couple of million dollars per rack, but it replaces but it replaces thousands of nodes. The amazing thing is, just the cables of connecting old general purpose computing systems cost more than replacing all of those and densifying into one rack. The benefit of densifying also is now that you've densified it, you can liquid cool it because it's hard to liquid cool a data center that's very large, but you can liquid cool the data center that's very small. And so the first thing that we're doing is accelerating, modernizing data centers, accelerating it, densifying it, making it more energy efficient. You save money, you save power, you save -- much more efficient. That's the first -- if we just focused on that, that's the next 10 years, we'll just accelerate that. Now, of course, there's a second dynamic, is because of NVIDIA's accelerated computing brought such enormous cost reductions to computing, it's like in the last 10 years, instead of Moore's Law being 100x, we scaled computing by 1000000x in the last 10 years. And so the question is, what would you do different if your plane traveled a million times faster? What would you do different if -- and so all of a sudden, people said, hey, listen, why don't we just use computers to write software? Instead of us trying to figure out what the features are, instead of us trying to figure out what the algorithms are, we'll just give the data, all the data -- all the predictive data to the computer and let it figure out what the algorithm is. Machine learning, Generative AI. And so we did it in such large scale on so many different data domains that now computers understand not just how to process the data, but the meaning of the data. And because it understands multiple modalities, at the same time, it can translate data. And so it can go from English to images, images to English, English to proteins, proteins to chemicals. And so because it understood all of the data at one time, it can now do all this translation. We call it Generative AI. Large amount of text into small amount of text, small amount of text into large amount of text, and so on and so forth. We're now in this computer revolution. And now, what's amazing is, so the first trillion dollars of data centers is going to get accelerated and invented this new type of software called Generative AI. This Generative AI is not just a tool, it is a skill. And so this is the interesting thing. This is why a new industry has been created. And the reason for that is, if you look at the whole IT industry, up until now, we've been making instruments and tools that people use. For the very first time, we're going to create skills that augment people. And so that's why people think that AI is going to expand beyond the trillion dollars of data centers and IT, and into the world of skills. So what's a skill? A digital chauffeur is a skill, autonomous, a digital assembly line worker, robot, a digital customer service, chatbot, digital employee for planning NVIDIA's supply chain. It could be a -- that would be somebody that's a digital SAP agent. There is a -- we use a lot of service now in our companies and we have digital employee service. And so now we have all these digital humans, essentially. And that's the wave of AI that we're in now.

Toshiya Hari 敏也针

So, step back, shift a little. Based on everything you just said, there's definitely an ongoing debate in financial markets as to whether or not, as we continue to build this AI infrastructure, there is an adequate return on investment.

所以，退后一步，稍微调整一下。根据你刚才所说的一切，金融市场上确实存在一个持续的辩论，即在我们继续构建这个人工智能基础设施的过程中，是否有足够的投资回报。

Jensen Huang 黄仁勋

Yeah. 是的。

Toshiya Hari 敏也针

How would you assess customer ROI at this point in the cycle? And if you look back and you kind of think about PCs, cloud computing, when they were at similar points in their adoption cycles, how did the ROIs look then compared to where we are now as we continue to scale?

您如何评估此时客户的投资回报率？如果回顾一下个人电脑和云计算在其采用周期的类似阶段时，投资回报率与我们现在在继续扩展时的情况相比如何？

Jensen Huang 黄仁勋

Yeah, fantastic. So, let's take a look. Before cloud, the major trend was virtualization, if you guys remember that. And virtualization basically said, let's take all of the hardware we have in the data center, let's virtualize it into essentially virtual data center, and then we could move workload across the data center instead of associating it directly to a particular computer. As a result, the tendency and the utilization of that data center improved. And we saw essentially a 2 to 1 -- 2.5 to 1, if you will, cost reduction in data centers overnight, virtualization. The second thing that we then said was after we virtualized that, we put those virtual computers right into the cloud. As a result, multiple companies, not just one company's many applications, multiple companies can share the same resource, another cost reduction, the utilization again went up. By the way, this last 10 years of all this -- 15 years of all this stuff happening, masked the fundamental dynamic which was happening underneath which is Moore's Law ending. We found a 2x -- another 2x in cost reduction, and it hid the end of the transistor scaling. It hid the transistor, the CPU scaling. Then all of a sudden, we already got the utilization cost reductions out of both of these things. We're now out. And that's the reason why we see data center and computing inflation happening right now. And so the first thing that's happening is accelerated computing. And so it's not uncommon for you to take your data processing work, and we -- there's a thing called Spark. If you -- anyone who’ve used -- Spark is probably the most used data processing engine in the world today. If you use Spark and you accelerate it with NVIDIA in the cloud, it's not unusual to see a 20 to 1 speed-up. And so you're going to save 10 -- and you pay, of course, you got it, the NVIDIA GPU augments the CPU, so the computing cost goes up a little bit. It goes -- maybe it doubles, but you reduce the computing time by about 20 times. And so you get a 10x savings. And it's not unusual to see this kind of ROI for accelerated computing. So I would encourage all of you, everything that you can accelerate -- to accelerate, and then once you accelerate it, run it with GPUs. And so that's the instant ROI that you get by acceleration. Now, beyond that, the Generative AI conversation is in the first wave of GenAI, which is where the infrastructure players like ourselves and all the cloud service providers put the infrastructure in the cloud so that developers could use these machines to train the models and fine-tune the models, guardrail the models, so on and so forth. And the return on that is fantastic because the demand is so great that for every dollar that they spend with us translates to $5 worth of rentals. And that's happening all over the world, and everything is all sold out. And so the demand for this is just incredible. Some of the applications that we already know about, of course, the famous ones, OpenAI's ChatGPT or GitHub Copilot, or code generators that we use in our company, the productivity gains are just incredible. There's not one software engineer in our company today who don't use code generators either the ones that we built ourselves for CUDA or USD, which is another language that we use in the company, or Verilog, or C and C++ and code generation. And so I think the days of every line of code being written by software engineers, those are completely over. And the idea that every one of our software engineers would essentially have companion digital engineers working with them 24/7, that's the future. And so the way I look at NVIDIA, we have 32,000 employees. Those 32,000 employees are surrounded by hopefully 100x more digital engineers.

Toshiya Hari 敏也针

Sure. Lots of industries embracing this. What cases -- use cases, industries are you most excited about?

当然。许多行业正在接受这一点。你最期待哪些案例——使用案例和行业？

Jensen Huang 黄仁勋

Well, in our company, we use it for computer graphics. We can't do computer graphics anymore without artificial intelligence. We compute one pixel, we infer the other 32. I mean, it's incredible. And so we hallucinate, if you will, the other 32, and it looks temporally stable, it looks photorealistic, and the image quality is incredible, the performance is incredible, the amount of energy we save -- computing one pixel takes a lot of energy. That's computation. Inferencing the other 32 takes very little energy, and you can do it incredibly fast. So one of the takeaways there is AI isn't just about training the model, of course, that's just the first step. It's about using the model. And so when you use the model, you save enormous amounts of energy, you save enormous amount of time -- processing time. So we use it for computer graphics. We -- if not for AI, we wouldn't be able to serve the autonomous vehicle industry. If not for AI, the work that we're doing in robotics, digital biology, just about every tech bio company that I meet these days are built on top of NVIDIA, and so they're using it for data processing or generating proteins for...

Toshiya Hari 敏也针

That seems like a super exciting space.

那看起来是一个超级令人兴奋的地方。

Jensen Huang 黄仁勋

It's incredible. Small molecule generation, virtual screening. I mean, just that whole space is going to get reinvented for the very first time with computer-aided drug discovery because of artificial intelligence. So, incredible work being done there.

这真是不可思议。小分子生成，虚拟筛选。我的意思是，整个领域将首次通过计算机辅助药物发现和人工智能进行重塑。因此，那里正在进行着令人难以置信的工作。

Toshiya Hari 敏也针

Yeah. Talk about competition, talk about your competitive moat. There's certainly group, public, and private companies looking to disrupt your leadership position. How do you think about your competitive moat?

是的。谈谈竞争，谈谈你的竞争护城河。确实有一些集团、公共和私人公司希望打破你的领导地位。你是如何看待你的竞争护城河的？

Jensen Huang 黄仁勋

Well, first of all, I think the -- I would say several things that are very different about us. The first thing is to remember that AI is not about a chip. AI is about an infrastructure. Today's computing is not build a chip and people come by your chips, put it into a computer, that's really kind of 1990s. The way that computers are built today, if you look at our new Blackwell system, we designed seven different types of chips to create the system. Blackwell is one of them.

首先，我认为我们之间有几个非常不同的地方。第一点是要记住，人工智能并不是关于一个芯片。人工智能是关于基础设施的。今天的计算并不是制造一个芯片，然后人们来购买你的芯片，把它放入计算机，这实际上有点像 1990 年代。如今计算机的构建方式，如果你看看我们的新 Blackwell 系统，我们设计了七种不同类型的芯片来创建这个系统。Blackwell 是其中之一。

Toshiya Hari 敏也针

I’m going to ask you to talk about Blackwell?

我想请你谈谈 Blackwell？

Jensen Huang 黄仁勋

Yeah. And so the amazing thing is, when you want to build this AI computer, people say words like super-cluster, infrastructure, supercomputer, for good reason because it's not a chip, it's not a computer per se. And so we're building entire data centers. By building the entire data center, if you just ever look at one of these superclusters, imagine the software that has to go into it to run it. There is no Microsoft Windows for it. Those days are over. So all the software that's inside that computer is completely bespoke. Somebody has to go write that. So the person who designs the chip and the company that designs that supercomputer, that supercluster and all the software that goes into it, it makes sense that it's the same company because it will be more optimized, they'll be more performant, more energy efficient, more cost effective. And so that's the first thing. The second thing is, AI is about algorithms. And we're really, really good at understanding what is the algorithm, what's the implication to the computing stack underneath and how do I distribute this computation across millions of processors, run it for days on date -- days on in, with the computer being as resilient as possible, achieving great energy efficiency, getting the job done as fast as possible, so on and so forth. And so we're really, really good at that. And then lastly, in the end, AI is computing. AI is software running on computers. And we know that the most important thing for computers is install base, having the same architecture across every cloud across on-prem to cloud, and having the same architecture available, whether you're building it in the cloud, in your own supercomputer, or trying to run it in your car or some robot or some PC, having that same identical architecture that runs all the same software is a big deal. It's called install base. And so the discipline that we've had for the last 30 years has really led to today. And it's the reason why the most obvious architecture to use if you were to start a company is to use NVIDIA's architecture. Because we're in every cloud, we're anywhere you like to buy it. And whatever computer you pick up, so long as it says NVIDIA inside, you know you can take the software and run it.

Toshiya Hari 敏也针

Yeah. You're innovating at an incredibly fast pace. I want you to talk a little bit more about Blackwell. Four times faster on training, 30 times faster inference than its predecessor Hopper. It just seems like you're innovating at such a quick pace. Can you keep up this rapid pace of innovation? And when you think about your partners, how do your partners keep up with the pace of innovation you're delivering?

是的。你们的创新速度非常快。我想让你多谈谈 Blackwell。在训练上比前身 Hopper 快四倍，推理速度快 30 倍。你们的创新似乎真的是如此迅速。你们能否保持这种快速的创新步伐？当你考虑到你们的合作伙伴时，他们是如何跟上你们所提供的创新步伐的？

Jensen Huang 黄仁勋

The pace of innovation, our basic methodology is to take -- because, remember, we're building an infrastructure. There's seven different chips. Each chip's rhythm is probably, at best, two years. At best, two years. We could give it a midlife kicker every year. But architecturally, if you're coming up with a new architecture every two years, you're running at the speed of light, okay? You're running insanely fast. Now, we have seven different chips, and they all contribute to the performance. And so we could innovate and bring a new AI cluster, a supercluster, to the market every single year that's better than the last generation because we have so many different pieces to work around. And so -- and the benefit of performance at the scale that we're doing, it directly translates the TCO. And so when Blackwell is three times the performance for somebody who has a given amount of power, say, 1 gigawatt, that's three times more revenues. That performance translates to throughput. That throughput translates to revenues. And so for somebody who has a gigawatt of power to use, you get three times the revenues. There's no way you can give somebody a cost reduction or discount on chips to make up for three times the revenues. And so the ability for us to deliver that much more performance through the integration of all these different parts and optimizing across the whole stack, and optimizing across the whole cluster, we can now deliver better and better value at much higher rates. The opposite of that is equally true. For any amount of money you want to spend, so for ISO power, you get three times the revenues. For ISO spend, you get three times the performance, which is another way of saying cost reduction. And so we have the best perf per watt, which is your revenues. We have the best perf per TCO, which means your gross margins. And so we keep pushing this out to the marketplace. Customers get to benefit from that not once every two years. And it's architecturally compatible. And so the software you developed yesterday will run tomorrow. The software you develop today will run across your entire install base. So we could run incredibly fast. If every single architecture was different, then you can't do this. It takes a year just to cobble together a system because we built everything together the day we ship it to you and it's pretty famous, somebody tweeted out that in 19 days after we shipped systems to them, they had a supercluster up and running, 19 days. You can't do that if you were cobbling together all these different chips and writing the software, you'll be lucky if you could do it in a year. And so I think our ability to transfer our innovation pace to customers, getting more revenues, getting better gross margins, that's a fantastic thing.

Toshiya Hari 敏也针

The majority of your supply chain partners operate out of Asia, particularly Taiwan. Given what's going on geopolitically, how you're thinking about that as you look forward?

大多数供应链合作伙伴在亚洲运营，特别是台湾。考虑到当前的地缘政治形势，您在展望未来时是如何看待这一点的？

Jensen Huang 黄仁勋

Yeah, the Asia supply chain, as you know, is really, really sprawling and interconnected. People think that when we say GPUs, because a long time ago, when I announced a new chip, a new generation of chips, I would hold up the chip. And so that was a new GPU. NVIDIA's new GPUs are 35,000 parts, weighs 80 pounds, consumes 10,000 amps. When you rack it up, it weighs 3,000 pounds. And so these GPUs are so complex, it's built like an electric car, components like an electric car. And so the ecosystem is really diverse and really interconnected in Asia. We try to design diversity and redundancy into every aspect, wherever we can. And then the last part of it is to have enough intellectual property in our company. In the event that we have to shift from one fab to another, we have the ability to do it. Maybe the process technology is not as great, maybe we won't be able to get the same level of performance or cost, but we will be able to provide the supply. And so I think the -- in the event anything were to happen, we should be able to pick up and fab it somewhere else. We're fabbing at a TSMC because it's the world's best and it's the world's best not by a small margin, it's the world's best by an incredible margin. And so not only just the long history of working with them, the great chemistry, their agility, the fact that they could scale, remember NVIDIA's last year's revenue had a major hockey stick. That major hockey stick wouldn't have been possible if not for the supply chain responding. And so the agility of that supply chain, including TSMC, is incredible. And in just less than a year, we've scaled up CoWoS capacity tremendously, and we're going to have to scale it up even more next year and scale up even more the year after that. But nonetheless, the agility and their capability to respond to our needs is just incredible. And so we use them because they're great, but if necessary, of course, we can always bring up others.

Toshiya Hari 敏也针

Yeah. Company is incredibly well-positioned. A lot of great stuff we've talked about. What do you worry about?

是的。公司处于非常有利的位置。我们谈论了很多很棒的事情。你担心什么？

Jensen Huang 黄仁勋

Well, our company works with every AI company in the world today. We're working with every single data center in the world today. I don't know, one data center, one cloud service provider, one computer maker we're not working with. And so what comes with that is enormous responsibility. And we have a lot of people on our shoulders, and everybody's counting on us. And demand is so great that delivery of our components and our technology and our infrastructure and software is really emotional for people because it directly affects their revenues, it directly affects their competitiveness. And so we probably have more emotional customers today than -- and deservedly so. If we could fulfill everybody's needs, then the emotion would go away. But it's very emotional, it's really tense. We've got a lot of responsibility on our shoulder, and we're trying to do the best we can. And here we are ramping Blackwell and it's in full production. We'll ship in Q4 and scale it -- start scaling in Q4 and into next year. And the demand on it is so great. And everybody wants to be first, and everybody wants to be most, and everybody wants to be -- and so the intensity is really, really quite extraordinary. And so I think it's fun to be inventing the next computer era. It's fun to see all these amazing applications being created. It's incredible to see robots walking around. It's incredible to have these digital agents coming together as a team, solving problems in your computer. It's amazing to see the AIs that we're using to design the chips that will run our AIs. All of that stuff is incredible to see. The part of it that is just really intense is just the world on our shoulders. And so less sleep is fine and three solid hours, that's all we need.

Toshiya Hari 敏也针

Well, good for you. I need more than that. I could spend another half-hour. Unfortunately, we've got to stop. Jensen, thank you very much thank you for being here and chatting with us today.

好吧，真为你高兴。我需要的不止这些。我可以再花半个小时。不幸的是，我们必须停止。詹森，非常感谢你今天能来和我们聊天。

Jensen Huang 黄仁勋

Thank you. Take care. 谢谢。保重。

Toshiya Hari 敏也针

Thank you. 谢谢。